title: Reconstructing spatially extended brain sources via enforcing multiple transform sparseness.
abstract: Accurate estimation of location and extent of neuronal sources from EEG/MEG remain challenging. In the present study, a new source imaging method, i.e. variation and wavelet based sparse source imaging (VW-SSI), is proposed to better estimate cortical source locations and extents. VW-SSI utilizes the L1-norm regularization method with the enforcement of transform sparseness in both variation and wavelet domains. The performance of the proposed method is assessed by both simulated and experimental MEG data, obtained from a language task and a motor task. Compared to L2-norm regularizations, VW-SSI demonstrates significantly improved capability in reconstructing multiple extended cortical sources with less spatial blurredness and less localization error. With the use of transform sparseness, VW-SSI overcomes the over-focused problem in classic SSI methods. With the use of two transformations, VW-SSI further indicates significantly better performance in estimating MEG source locations and extents than other SSI methods with single transformations. The present experimental results indicate that VW-SSI can successfully estimate neural sources (and their spatial coverage) located in close areas while responsible for different functions, i.e. temporal cortical sources for auditory and language processing, and sources on the pre-bank and post-bank of the central sulcus. Meantime, all other methods investigated in the present study fail to recover these phenomena. Precise estimation of cortical source locations and extents from EEG/MEG is of significance for applications in neuroscience and neurology.
authors.affiliation: School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA.
authors.forename: Min
authors.initials: M
authors.lastname: Zhu
authors.affiliation: Minnesota Epilepsy Group, P.A. at John Nasseff Neuroscience Institute, Allina Health, St Paul, MN, USA; Department of Neurology, University of Minnesota, Minneapolis, MN, USA.
authors.forename: Wenbo
authors.initials: W
authors.lastname: Zhang
authors.affiliation: Minnesota Epilepsy Group, P.A. at John Nasseff Neuroscience Institute, Allina Health, St Paul, MN, USA; Department of Neurology, University of Minnesota, Minneapolis, MN, USA.
authors.forename: Deanna L
authors.initials: DL
authors.lastname: Dickens
authors.affiliation: School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA; Center for Biomedical Engineering, University of Oklahoma, Norman, OK, USA; Laureate Institute for Brain Research, Tulsa, OK, USA. Electronic address: leiding@ou.edu.
authors.forename: Lei
authors.initials: L
authors.lastname: Ding
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D004569:Electroencephalography / Q000379:methods; D019054:Evoked Potentials, Motor / Q000502:physiology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015225:Magnetoencephalography / Q000379:methods*; D009068:Movement / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Extent; L1-norm regularization; MEG; Multiple penalties; Sparse source imaging; Transform sparseness